




"""simple docstring"""
from __future__ import annotations

from typing import Any

class __lowercase:




					'''simple docstring'''


					def __init__(     self    ,						__a    ,						__a    ,						__a = 0  ):
								__lowerCamelCase       :						Optional[int]													=					row, column
								__lowerCamelCase       :						Optional[Any]													=					[[default_value for c in range(__UpperCAmelCase  )] for r in range(__UpperCAmelCase  )]
					def __str__(     self  ):
								__lowerCamelCase       :						List[str]													=					f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__lowerCamelCase       :						Dict													=					0
								for row_vector in self.array:
											for obj in row_vector:
														__lowerCamelCase       :						Dict													=					max(__UpperCAmelCase    ,						len(str(__UpperCAmelCase  )  )  )
								__lowerCamelCase       :						Any													=					f'''%{max_element_length}s'''

								# Make string and return
								def single_line(__a  ) -> str:
											nonlocal string_format_identifier
											__lowerCamelCase       :						List[str]													=					'''['''
											line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
											line += "]"
											return line

								s += "\n".join(single_line(__UpperCAmelCase  ) for row_vector in self.array  )
								return s
					def __repr__(     self  ):
								return str(self  )
					def      snake_case_					(     self    ,						__a  ):
								if not (isinstance(__UpperCAmelCase    ,						(list, tuple)  ) and len(__UpperCAmelCase  ) == 2):
											return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
											return False
								else:
											return True
					def __getitem__(     self    ,						__a  ):
								assert self.validate_indicies(__UpperCAmelCase  )
								return self.array[loc[0]][loc[1]]
					def __setitem__(     self    ,						__a    ,						__a  ):
								assert self.validate_indicies(__UpperCAmelCase  )
								__lowerCamelCase       :						str													=					value
					def __add__(     self    ,						__a  ):
								assert isinstance(__UpperCAmelCase    ,						__UpperCAmelCase  )
								assert self.row == another.row and self.column == another.column

								# Add
								__lowerCamelCase       :						Union[str, Any]													=					Matrix(self.row    ,						self.column  )
								for r in range(self.row  ):
											for c in range(self.column  ):
														__lowerCamelCase       :						int													=					self[r, c] + another[r, c]
								return result
					def __neg__(     self  ):
								__lowerCamelCase       :						Tuple													=					Matrix(self.row    ,						self.column  )
								for r in range(self.row  ):
											for c in range(self.column  ):
														__lowerCamelCase       :						List[str]													=					-self[r, c]
								return result
					def __sub__(     self    ,						__a  ):
								return self + (-another)
					def __mul__(     self    ,						__a  ):

								if isinstance(__UpperCAmelCase    ,						(int, float)  ):  # Scalar multiplication
											__lowerCamelCase       :						str													=					Matrix(self.row    ,						self.column  )
											for r in range(self.row  ):
														for c in range(self.column  ):
																	__lowerCamelCase       :						List[str]													=					self[r, c] * another
											return result
								elif isinstance(__UpperCAmelCase    ,						__UpperCAmelCase  ):  # Matrix multiplication
											assert self.column == another.row
											__lowerCamelCase       :						Any													=					Matrix(self.row    ,						another.column  )
											for r in range(self.row  ):
														for c in range(another.column  ):
																	for i in range(self.column  ):
																				result[r, c] += self[r, i] * another[i, c]
											return result
								else:
											__lowerCamelCase       :						Union[str, Any]													=					f'''Unsupported type given for another ({type(__UpperCAmelCase  )})'''
											raise TypeError(__UpperCAmelCase  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					Matrix(self.column    ,						self.row  )
								for r in range(self.row  ):
											for c in range(self.column  ):
														__lowerCamelCase       :						Tuple													=					self[r, c]
								return result





					def      snake_case_					(     self    ,						__a    ,						__a  ):
								assert isinstance(__UpperCAmelCase    ,						__UpperCAmelCase  ) and isinstance(__UpperCAmelCase    ,						__UpperCAmelCase  )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								__lowerCamelCase       :						Optional[Any]													=					v.transpose()
								__lowerCamelCase       :						Any													=					(v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
											return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





					def     UpperCAmelCase					(      )	->  str:
								# a^(-1)
								__lowerCamelCase       :						Any													=					Matrix(3				,					3				,					0  )
								for i in range(3  ):
											__lowerCamelCase       :						int													=					1
								print(f'''a^(-1) is {ainv}'''  )
								# u, v
								__lowerCamelCase       :						Union[str, Any]													=					Matrix(3				,					1				,					0  )
								__lowerCamelCase       :						Union[str, Any]													=					1, 2, -3
								__lowerCamelCase       :						Union[str, Any]													=					Matrix(3				,					1				,					0  )
								__lowerCamelCase       :						Optional[int]													=					4, -2, 5
								print(f'''u is {u}'''  )
								print(f'''v is {v}'''  )
								print(f'''uv^T is {u * v.transpose()}'''  )
								# Sherman Morrison
								print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase_				,					lowerCamelCase_  )}'''  )





					def     UpperCAmelCase					(      )	->  Dict:
								import doctest

								doctest.testmod()






					testa()






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	'''pixel_values'''
							UpperCAmelCase_						:		List[str]								=	False
							UpperCAmelCase_						:		Tuple								=	TimmBackboneConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														requires_backends(self    ,						'''timm'''						)
														super().__init__(__UpperCAmelCase						)
														A      :					List[str]      		=					config

														if config.backbone is None:
																					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''						)

														if config.backbone not in timm.list_models():
																					raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

														if hasattr(__UpperCAmelCase    ,						'''out_features'''						) and config.out_features is not None:
																					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''						)

														A      :					str      		=					getattr(__UpperCAmelCase    ,						'''use_pretrained_backbone'''    ,						__UpperCAmelCase						)
														if pretrained is None:
																					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''						)

														# We just take the final layer by default. This matches the default for the transformers models.
														A      :					Optional[int]      		=					config.out_indices if getattr(__UpperCAmelCase    ,						'''out_indices'''    ,						__UpperCAmelCase						) is not None else (-1,)

														A      :					str      		=					timm.create_model(
														    config.backbone    ,						pretrained=__UpperCAmelCase    ,						features_only=config.features_only    ,						in_chans=config.num_channels    ,						out_indices=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														A      :					str      		=					self._backbone.return_layers
														A      :					Any      		=					{layer['''module''']: str(__UpperCAmelCase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
														super()._init_backbone(__UpperCAmelCase						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														requires_backends(cls    ,						['''vision''', '''timm''']						)
														from ...models.timm_backbone import TimmBackboneConfig

														A      :					Optional[Any]      		=					kwargs.pop('''config'''    ,						TimmBackboneConfig()						)

														A      :					str      		=					kwargs.pop('''use_timm_backbone'''    ,						__UpperCAmelCase						)
														if not use_timm:
																					raise ValueError('''use_timm_backbone must be True for timm backbones'''						)

														A      :					Optional[int]      		=					kwargs.pop('''num_channels'''    ,						config.num_channels						)
														A      :					List[str]      		=					kwargs.pop('''features_only'''    ,						config.features_only						)
														A      :					Optional[int]      		=					kwargs.pop('''use_pretrained_backbone'''    ,						config.use_pretrained_backbone						)
														A      :					Optional[int]      		=					kwargs.pop('''out_indices'''    ,						config.out_indices						)
														A      :					int      		=					TimmBackboneConfig(
														    backbone=__UpperCAmelCase    ,						num_channels=__UpperCAmelCase    ,						features_only=__UpperCAmelCase    ,						use_pretrained_backbone=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						)
														return super()._from_config(__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														pass






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       Union[BackboneOutput, Tuple[Tensor, ...]]:
														A      :					Optional[int]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					str      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					Any      		=					output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError('''Cannot output attentions for timm backbones at the moment'''						)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					A      :					Dict      		=					self._all_layers
																					A      :					int      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Any      		=					self._return_layers
																					A      :					Union[str, Any]      		=					tuple(hidden_states[i] for i in self.out_indices						)
														else:
																					A      :					Optional[int]      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					List[Any]      		=					None

														A      :					str      		=					tuple(__UpperCAmelCase						)
														A      :					int      		=					tuple(__UpperCAmelCase						) if hidden_states is not None else None

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												A      :					Tuple      		=					output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						attentions=__UpperCAmelCase						)






